ROA, ROE and EPS data for the distressed and healthy companies collected and compared with one another. We found that none of the three performance measures could distinguish one group from the other statistically two years before the crisis. However,
significant differences were found one year before the crisis. To look further into the relationship between the ownership and board structure variables and financial performance variable, we constructed an overall performance index. Each firm gets a rank in each of the three financial measures one year before financial distress. The sums of the three ranks were ranked again. The first half of the overall ranking was grouped into a good performance sample while the last half was called the bad performance sample.
Logistical regressions were then run on both the good sample (consisting of 15 distressed companies and 52 healthy companies) and the bad sample (consisting of 30 distressed companies and 36 healthy companies). The results are shown in Tables 6 and 7, respectively.
For the good sample, the board structure variables are more capable of explaining the financial distress. Specifically, when the controlling shareholder holds more seats on the board, even good performance companies receive a higher probability for distress next year.
On the contrary, when more directors are held by the non-large shareholders, that probability for distress is reduced. Similar to Table 2, founder participation also signals a lower probability for financial distress. The implication is that even good performance companies are likely to get into financial trouble later on if corporate governance is weakened.
Insert Table 6 here
Insert Table 7 here
For the bad performance sample, Table 7 does not show any significant explanatory power for the board structure variables. Debt ratio, adjusted control rights and cash-to-control rights ratio represent more powerful explanatory variables. The implication is that when a bad performance company increases its' debt ratio, lowers its' adjusted control rights (through higher pledge ratio), and widens the discrepancies between its' control and cash flow rights,
the probability of financial distress in the following year will inevitably increase.
VI. Conclusions
Taiwanese listed companies are typically controlled by families. The ownership and board structures are similar to those in many other countries. The anti-director rights index for Taiwanese listed firms resembles the average index of 49 countries investigated by La Porta et al. (1998). Our empirical results, therefore, may shed some light on the effects of corporate governance and the likelihood of financial distress for other countries.
The corporate governance variables used include the deviation of control rights from cash flow rights, the percentage of board (supervisors) seats controlled by the largest shareholder, and the percentage of shares pledged for loans by board members and managers.
Our results suggest that the greater the deviation of control rights from cash flow rights, the more directors and supervisors occupied by the largest shareholder and the higher the stock pledge ratio, the more likelihood the firm would get into financial distress in the following year. Similar results were obtained even after controlling for the effects that debt ratio and financial performance may have. A holdout sample prediction, based on the logistical models developed using estimated sample, gives an average probability for getting into financial distress of 63.09% for those companies that actually fell into crisis, and 23.68% for those that remained financially healthy.
The argument that the controlling shareholder may desire to prolong the expropriation honeymoon suggests that poor governance may not lead to higher probability of financial failure. However, since our sampling period essentially covers the Asian Financial Crisis which led to serious economic recession, poor macroeconomic factors might have speeded up the occurrence of financial distress even if the expropriative shareholders tried to prevent it
from happening. Furthermore, the Bankruptcy Law of Taiwan does not require the removal of the controlling shareholder from the managerial positions, which practically reduces the cost of financial distress. It follows that the controlling shareholder may not try too hard enough to fight for survival as the hypothesis of expropriation prolongation predicts.
Several researches have cited bad corporate governance as one of the key factors leading to the Asian Financial Crisis in 1997. However, no one has tried to develop an early warning system that incorporates corporate governance variables so far. Our research results indicate that an early warning system cannot be complete without incorporating the corporate governance characteristics. The reason is that poor corporate governance can increase the probability of financial distress even for firms with good financial performance. Therefore, financial data alone may not be good enough for the purpose of predicting financial failure.
Rich policy implications may also be derived from our research. For example, strengthening the corporate governance mechanism would help to reduce the likelihood of financial failures, especially in a low cash-to-control rights ratio environment.
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Table 1: Basic Statistics of Ownership structure and board compositions one and two years before the financial distress
We collected data from Taiwan listed companies that encountered financial distress between January 1996 and December 1999, together with a matching sample consisting of healthy companies. We have 45 companies in our financial distress sample. The matching samples were chosen on a two-to-one basis consisting of firms that were in the same industry and of comparable size, but not going into financial distress during the sampling period. The sampling technique employed helped to control the influences of industry and size factors on financial distress. 88 firms were chosen as our matching sample.
one year before two years before
average (%) average (%)
***: significant at 1% level **: significant at 5% level *: significant at 10% level
Table 2: Regression coefficients of logistical models - the year prior to financial distress, all samples
Binary logistical regression models were employed to investigate the relationship between ownership and board structure and the likelihood of financial distress one-year before the financial distress. The dependent variable was binary with the value one indicating financially distressed firms, and zero indicating financially healthy firms.
Independent variable Dependent variable = 1, if financial distress occurs 0, otherwise Adjusted control rights -0.059
(8.806)***
Cash-control right ratio -0.110 (3.719)*
Shareholding of the second largest shareholder Shareholding of institutionals -0.001
(0.002) Directors assumed by the largest
shareholder
0.023 (3.454)*
Supervisors assumed by the largest shareholder
(2) The ratio of R&D expenses and advertisement expenses to sales.
***: significant at 1% level **: significant at 5% level *: significant at 10% level
Table 3: Logistical model regression coefficients - two years prior to financial distress, all samples
Binary logistical regression models were employed to investigate the relationship between ownership and board structure and the likelihood of financial distress two years before the financial distress. The dependent variable was binary with the value one indicating financially distressed firms, and zero indicating financially healthy firms.
Independent variable Dependent variable = 1, if financial distress occurs 0, otherwise Adjusted control rights -0.0989
(13.824)***
Cash-control right ratio -0.513 (0.001) Shareholding of the second largest
shareholder Shareholding of institutionals -0.0213
(1.253) Directors assumed by the largest
shareholder
0.011 (0.952) Supervisors assumed by the largest
shareholder
(2) The ratio of R&D expenses and advertisement expenses to sales.
***: significant at 1% level **: significant at 5% level *: significant at 10% level
Table 4: Estimated probabilities of financial distress for the holdout sample - one year before the financial distress
For the purpose of prediction, the first two thirds of our sample (which included 30 distressed firms and 58 healthy matching firms) was designated as the estimated sample according to the time-series order of the occurrence of the financial distress. The remaining third (which included 15 distressed firms and 30 healthy matching firms), or the holdout sample was reserved to validate the statistical results generated from the estimated sample. Similar logistical regressions were run on the estimated sample using the data one-year before the distress to generate parameter estimates. The data for the holdout sample one-year before the crisis were then plugged into the estimated model.
Estimated probability of financial distress Independent variable in
the estimated model(1) financial distressed firms financially healthy firms t-statistics(3)
% of director occupied by the largest shareholder, and
other independent variables(2)
0.7237 0.3198 5.487***
% of supervisors occupied by the largest shareholder,
and other independent variables
0.6171 0.2273 5.458***
% of director held by non-large shareholders and other independent variables
0.5899 0.1937 4.879***
% of supervisors held by non-large shareholders and other independent variables
0.5929 0.2063 5.441***
Average 0.6309 0.2368
(1) Estimated models are logistical regression models that were estimated using the data one-year before the financial distress.
(2) Other independent variables refer to all the independent variables in Table 2 except the percentage of directors (supervisors) held by the controlling shareholders and non-large shareholders, respectively.
(3) T-statistics were computed under the hypothesis that there is no difference in the estimated probabilities of financial distress for both groups of companies.
***: significant at 1% level
Table 5: Misclassification of the holdout samples
This study applied the cutoff point of 0.5 (the probability of financial distress) to investigate the number of cases of holdout samples that were misjudged. For the first model (using percentage of directors held by the largest shareholder as explanatory variable), three of the 15 distressed companies were misclassified as healthy firms, and five of the other 30 healthy firms were misclassified as distressed firms. For the other three models, more misclassified cases were found for the distressed group, but less for the healthy group.
holdout sample
(1) Other independent variables refer to all of the independent variables in Table 2 except for the percentage of directors (supervisors) held by the controlling shareholders and non-large shareholders, respectively.
Table 6: The impacts of owne rship and board structures on the probability of financial distress - logistical model for good performance companies, one year before the financial distress
We constructed an overall performance index using ROA, ROE and EPS. Each firm received a rank in each of the three financial measures. The sums of the three ranks were ranked again.
The first half of the overall ranking was grouped into a good performance sample while the last half was called the bad performance sample. Logistical regressions were then run on the good sample (consisting of 15 distressed companies and 52 healthy companies). The dependent variable was binary with the value one indicating financially distressed firms, and zero indicating financially healthy firms.
Independent variable Dependent variable = 1, if financial distress occurs 0, otherwise Adjusted control rights -0.054
(2.801)* Cash-control right ratio -0.049
(0.247) Shareholding of the second largest
shareholder Shareholding of institutionals -0.003
(0.012) Directors assumed by the largest
shareholder
0.041 (3.073)*
Supervisors assumed by the largest shareholder
Numbers in parentheses are x2 values.
***: significant at 1% level **: significant at 5% level *: significant at 10% level
Table 7: The impacts of ownership and board structures on the probability of financial distress - logistical model for bad performance companies, one year before the financial distress
We constructed an overall performance index using ROA, ROE and EPS. Each firm received a rank in each of the three financial measures. The sums of the three ranks were ranked again.
The first half of the overall ranking was grouped into a good performance sample while the last half was called the bad performance sample. Logistical regressions were then run on the bad sample (consisting of 30 distressed companies and 36 healthy companies). The dependent variable was binary with the value one indicating financially distressed firms, and zero indicating financially healthy firms.
Independent variable Dependent variable = 1, if financial distress occurs 0, otherwise
Adjusted control rights -0.066 (4.461)**
Cash-control right ratio -0.229 (3.564)*
Shareholding of the second largest shareholder Shareholding of institutionals -0.002
(0.001) Directors assumed by the largest
shareholder
-0.004 (0.043) Supervisors assumed by the largest
shareholder
Numbers in parentheses are X2 values.
***: significant at 1% level **: significant at 5% level *: significant at 10% level